The available spectrum resource for radio communication systems is a significantly limited and expensive resource. Furthermore, the spectrum bands do not all have the same economic potential for use by commercial radio communication systems. Indeed, the most useful frequencies (300 MHz-3 GHz) are currently almost entirely allocated to existing systems. However, several measurement campaigns conducted in several countries have shown that many of the allocated bands tend to be only sparsely used, i.e. not all the frequency resource is used all the time and everywhere.
Thus, spectrum availability holes tend to exist in the allocated bands allowing for a potential reuse of such unused resource between various systems. Due to the tremendous increase in the demand for communication resource for wireless systems, a more flexible allocation of the spectrum resource is a high priority for regulators and operators in order to provide a more efficient use of the available resource.
However, current spectrum resource planning techniques tend to lead to over dimensioned networks with underused spectrum. Furthermore, conventional resource planning tends to be inflexible and use large centralised decision processes resulting in slow and resource demanding adaptation of a system to the current conditions.
Wireless terminals have gained more and more computation capabilities in recent years. In the near future, cognitive radios will be environment-aware, i.e. they will e.g. be able to sense their local environment, to communicate with other communication units using several air interfaces and to adapt to local environment variations. To reach such an increased level of intelligence, smart algorithms for spectrum allocation are desired and in particular a dynamic and distributed planning is desired.
The application of distributed resource allocation processes exploiting such computational capabilities can promise improved frequency resource allocation. However, although a system employing distributed and dynamic frequency management may be able to adapt the spectrum allocation to the current conditions and spatial distribution of users, it requires robust and flexible algorithms.
An example of distributed frequency resource management in an Orthogonal Frequency Division Multiplex (OFDM) system is disclosed in “Efficient OFDMA distributed optimization algorithm exploiting multi-user diversity” by J. C. Dunat, D. Grandblaise, and C. Bonnet, DySPAN 2005, 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Nov. 8-11, 2005, Baltimore, US.
The described system uses a distributed decision process wherein a communication unit interested in transmitting data to a central communication unit transmits an indication of the air interface channel quality to the central communication unit. The central communication unit then broadcasts information of the received channel qualities for a given time slot and the individual communication nodes independently decide whether to use the specific time slot depending on the information from the central communication unit. The system uses a distributed meta-heuristic inspired from the observation of the behaviour of social insects for distributed tasks allocation. Such methods of swarm intelligence require no external control to converge to a global optimum using only local interactions of agents.
However, the described system is relatively inflexible and although it can provide efficient resource utilisation within a single cell, the resource utilisation of the system as a whole is not necessarily optimised.
Hence, an improved OFDM communication system would be advantageous and in particular a system allowing increased flexibility, facilitated resource management, reduced complexity and/or improved resource utilisation would be advantageous.